scikit-learn · adrinjalali · Apr 15, 2024 · Feb 12, 2024 · Feb 12, 2024 · Feb 12, 2024
diff --git a/doc/common_pitfalls.rst b/doc/common_pitfalls.rst
@@ -408,43 +408,40 @@ it will allow the estimator RNG to vary for each fold.
     illustration purpose: what matters is what we pass to the
     :class:`~sklearn.ensemble.RandomForestClassifier` estimator.
 
-|details-start|
-**Cloning**
-|details-split|
+.. dropdown:: Cloning
+
+    Another subtle side effect of passing `RandomState` instances is how
+    :func:`~sklearn.base.clone` will work::
+
+        >>> from sklearn import clone
+        >>> from sklearn.ensemble import RandomForestClassifier
+        >>> import numpy as np
+
+        >>> rng = np.random.RandomState(0)
+        >>> a = RandomForestClassifier(random_state=rng)
+        >>> b = clone(a)
+
+    Since a `RandomState` instance was passed to `a`, `a` and `b` are not clones
+    in the strict sense, but rather clones in the statistical sense: `a` and `b`
+    will still be different models, even when calling `fit(X, y)` on the same
+    data. Moreover, `a` and `b` will influence each-other since they share the
+    same internal RNG: calling `a.fit` will consume `b`'s RNG, and calling
+    `b.fit` will consume `a`'s RNG, since they are the same. This bit is true for
+    any estimators that share a `random_state` parameter; it is not specific to
+    clones.
+
+    If an integer were passed, `a` and `b` would be exact clones and they would not
+    influence each other.
+
+    .. warning::
+        Even though :func:`~sklearn.base.clone` is rarely used in user code, it is
+        called pervasively throughout scikit-learn codebase: in particular, most
+        meta-estimators that accept non-fitted estimators call
+        :func:`~sklearn.base.clone` internally
+        (:class:`~sklearn.model_selection.GridSearchCV`,
+        :class:`~sklearn.ensemble.StackingClassifier`,
+        :class:`~sklearn.calibration.CalibratedClassifierCV`, etc.).
 
-Another subtle side effect of passing `RandomState` instances is how
-:func:`~sklearn.base.clone` will work::
-
-    >>> from sklearn import clone
-    >>> from sklearn.ensemble import RandomForestClassifier
-    >>> import numpy as np
-
-    >>> rng = np.random.RandomState(0)
-    >>> a = RandomForestClassifier(random_state=rng)
-    >>> b = clone(a)
-
-Since a `RandomState` instance was passed to `a`, `a` and `b` are not clones
-in the strict sense, but rather clones in the statistical sense: `a` and `b`
-will still be different models, even when calling `fit(X, y)` on the same
-data. Moreover, `a` and `b` will influence each-other since they share the
-same internal RNG: calling `a.fit` will consume `b`'s RNG, and calling
-`b.fit` will consume `a`'s RNG, since they are the same. This bit is true for
-any estimators that share a `random_state` parameter; it is not specific to
-clones.
-
-If an integer were passed, `a` and `b` would be exact clones and they would not
-influence each other.
-
-.. warning::
-    Even though :func:`~sklearn.base.clone` is rarely used in user code, it is
-    called pervasively throughout scikit-learn codebase: in particular, most
-    meta-estimators that accept non-fitted estimators call
-    :func:`~sklearn.base.clone` internally
-    (:class:`~sklearn.model_selection.GridSearchCV`,
-    :class:`~sklearn.ensemble.StackingClassifier`,
-    :class:`~sklearn.calibration.CalibratedClassifierCV`, etc.).
-
-|details-end|
 
 CV splitters
 ............

diff --git a/doc/conf.py b/doc/conf.py
@@ -66,6 +66,7 @@
     # See sphinxext/
     "allow_nan_estimators",
     "doi_role",
+    "dropdown_anchors",
     "move_gallery_links",
     "sphinx_issues",
 ]
@@ -316,7 +317,7 @@
 
 # Additional JS files
 html_js_files = [
-    "scripts/details-permalink.js",
+    "scripts/dropdown.js",
     "scripts/version-switcher.js",
 ]
 
@@ -432,28 +433,6 @@ def add_js_css_files(app, pagename, templatename, context, doctree):
 html_show_search_summary = True
 
 
-# The "summary-anchor" IDs will be overwritten via JavaScript to be unique.
-# See `doc/js/scripts/details-permalink.js`.
-rst_prolog = """
-.. |details-start| raw:: html
-
-    <details id="summary-anchor">
-    <summary class="btn btn-light">
-
-.. |details-split| raw:: html
-
-    <span class="tooltiptext">Click for more details</span>
-    <a class="headerlink" href="#summary-anchor" title="Permalink to this heading">¶</a>
-    </summary>
-    <div class="card">
-
-.. |details-end| raw:: html
-
-    </div>
-    </details>
-
-"""
-
 # -- Options for LaTeX output ------------------------------------------------
 latex_elements = {
     # The paper size ('letterpaper' or 'a4paper').
@@ -759,7 +738,7 @@ def setup(app):
     # triggered just before the HTML for an individual page is created
     app.connect("html-page-context", add_js_css_files)
 
-    # to hide/show the prompt in code examples:
+    # to hide/show the prompt in code examples
     app.connect("build-finished", make_carousel_thumbs)
     app.connect("build-finished", filter_search_index)
 

diff --git a/doc/datasets/loading_other_datasets.rst b/doc/datasets/loading_other_datasets.rst
@@ -33,9 +33,9 @@ and pipelines on 2D data.
   if you plan to use ``matplotlib.pyplpt.imshow``, don't forget to scale to the range
   0 - 1 as done in the following example.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py`
+* :ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py`
 
 .. _libsvm_loader:
 
@@ -68,11 +68,10 @@ features::
   ...     "/path/to/test_dataset.txt", n_features=X_train.shape[1])
   ...                                                         # doctest: +SKIP
 
-.. topic:: Related links:
+.. rubric:: Related links
 
- _`Public datasets in svmlight / libsvm format`: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets
-
- _`Faster API-compatible implementation`: https://github.com/mblondel/svmlight-loader
+- `Public datasets in svmlight / libsvm format`: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets
+- `Faster API-compatible implementation`: https://github.com/mblondel/svmlight-loader
 
 ..
     For doctests:
@@ -215,11 +214,11 @@ identifies the dataset::
   '969'
 
 
-.. topic:: References:
+.. rubric:: References
 
- * :arxiv:`Vanschoren, van Rijn, Bischl and Torgo. "OpenML: networked science in
-   machine learning" ACM SIGKDD Explorations Newsletter, 15(2), 49-60, 2014.
-   <1407.7722>`
+* :arxiv:`Vanschoren, van Rijn, Bischl and Torgo. "OpenML: networked science in
+  machine learning" ACM SIGKDD Explorations Newsletter, 15(2), 49-60, 2014.
+  <1407.7722>`
 
 .. _openml_parser: